Systems | Development | Analytics | API | Testing

New hires always have questions, and waiting on a response slows people down on day one. In this video, we use AgentSpot to build an Employee Policy Helpdesk Agent that pulls from your internal knowledge base and policy pages to answer everyday HR questions accurately, without paraphrasing or guessing. It knows when to escalate to a human and deploys directly in Slack so employees get answers right where they already work.

Watch how AgentSpot builds a Release Notes workflow that checks GitHub every morning for new staging releases, gathers the merged PRs behind them, rewrites the whole lot into benefit-first, jargon-free release notes, and publishes them as a Slack Canvas with a short summary posted to your channel, so your team learns what shipped without anyone hand-writing it. What is AgentSpot? AgentSpot is an Agentic Workforce Platform for building workflows that make decisions, take action, and deliver results across every system your business runs on, grounded in your data.

Watch how AgentSpot builds an Opportunity Planning Agent that reads your validation brief in Confluence, queries your GTM and product models in ThoughtSpot for the evidence behind it, weighs the build options against real pipeline and roadmap themes, and publishes a decision-ready brief with a named bet, a priority call, and a now-next-later path, so your next planning cycle starts from evidence instead of opinion.

Watch how AgentSpot builds a Product Discovery Agent that reads support tickets, sales calls, Slack threads, CRM notes and product analytics, clusters what customers keep raising into ranked themes, and hands your PMs the pattern, the accounts affected, the evidence behind it and what to do next. What is AgentSpot? AgentSpot is an Agentic Workforce Platform for building workflows that make decisions, take action, and deliver results across every system your business runs on, grounded in your data.

Semantic layers and ontologies have moved from nice-to-have data modeling tools to the foundational engine required for enterprise AI. In this episode, Raman Tallamraju, Senior Director and Head of Enterprise Data Architecture and Engineering at Vanguard, breaks down how Vanguard is architecting its AI semantic layer to turn scattered institutional knowledge into reliable, agent-ready context. He shares why autonomous agents expose decades of hidden data debt, how to bridge domain-specific definitions like clients versus prospects, and how to balance building a unified semantic layer with a pragmatic, federated data operating model.

AI didn't remove the release bottleneck, it moved it downstream. Code volume exploded, verification didn't. The old QA model isn't broken, it's outgrown: 80% of engineering teams have already traced a production incident to AI-generated code. AURA is Sauce Labs' answer, the only full-lifecycle release assurance platform built to close the gap. It continuously verifies every release against business intent, authoring, running, and regenerating tests in an autonomous learning loop, with humans in control.

DreamFactory 7.7 is out. Agents have owners. API Builder is new. Schema Contracts lock the shape. The admin console is new. Four things that shipped: Agent governance. Every agent has a human owner, a role, and a key that expires in four hours. Deactivate the owner and the agent stops. API Builder. Design the endpoint the app actually wants. Custom paths, shaped responses, on services you already have. Open source, in every edition.

Every tech leader is talking about agentic development and the promise of dramatically speeding up software delivery. But as organizations begin scaling AI-generated code, many are running into the same challenge: their test data processes can't keep up.

The challenge for most data teams isn’t a lack of ideas—it’s the time it takes to turn those ideas into something usable. In this session, Steffen Bischoff, Chief Architect Data at Qlik, follows a single dataset from a core system through its entire journey to becoming a governed data product. You’ll see pipelines created by describing intent instead of writing code, versioned in Git, then curated, quality-checked, and documented with the help of specialized agents. From there, the data product is made available to the AI tool of your choice through the Qlik MCP Server.

Use Qlik Answers to manage and create your Data Products!